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Oak Ridge National Laboratory researchers developed LuGo, an algorithm for quantum phase estimation in a fluid-flow modeling problem. The team reports that it reduced the required quantum gates from 2 million to 91,000; the work was validated in classical simulations and evaluated on quantum processors, but no practical speedup for real-world flow models is established.
Oak Ridge National Laboratory researchers developed LuGo, an algorithm that they say cut the number of quantum logic gates needed for a fluid-flow modeling problem from 2 million to 91,000. The reduction addresses a major source of noise in the quantum calculation, though the team has not established that LuGo speeds up practical fluid simulations.
LuGo is designed for quantum phase estimation, the process used to encode information from an equation into a quantum circuit. In the team’s earlier work on the Hele-Shaw flow equation, this step accounted for more than 90% of the computational effort. LuGo shifts more calculations onto classical computers before data enters the quantum circuit, reducing the circuit’s gate requirement by more than 95%, according to the report.
The researchers validated the algorithm by running classical simulations of the quantum circuits. They also evaluated it on quantum processors accessed through the Department of Energy’s Quantum Computing User Program: Quantinuum’s H-1, IBM’s Marrakesh and Sherbrooke, and IQM’s Garnet and Sirius. The source material does not provide processor-specific results or a measured speedup over conventional simulation.
The work was supported by computing allocations on Oak Ridge’s Frontier supercomputer and the National Energy Research Scientific Computing Center’s Perlmutter system. The study was presented at the 2025 IEEE International Conference on Quantum Computing and Engineering. The report says LuGo received a 2026 R&D 100 Award.
Fewer Gates Could Reduce Circuit Noise
Quantum gates are operations that make up a circuit. Each one adds another opportunity for errors, and qubit noise is a persistent limit on current quantum hardware. Reducing the gate count for this calculation could make it easier to run the problem on available processors or to study it as hardware improves.
The potential relevance extends beyond this test case. The researchers point to microfluidics, groundwater flow and porous media flow as possible areas for future applications. Those are prospects, not demonstrated outcomes: the reported gate reduction alone does not show that LuGo produces faster, more accurate, or larger real-world fluid simulations.
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From Hele-Shaw Flow to Quantum Circuits
The study builds on earlier ORNL research into applying the Harrow-Hassidim-Lloyd algorithm, a quantum method for solving systems of linear equations, to the Hele-Shaw equation. That equation describes liquid or gas moving between two closely spaced parallel plates. The ORNL team identified the conversion of the equation into quantum form as the main computational choke point.
Fluid-flow modeling matters in fields such as aerodynamics and oil refining, where flow over equipment can affect performance. Detailed simulations can demand substantial computing resources; the source cites an ORNL ocean-flow simulation that used trillions of grid points. Quantum computers encode information differently from classical systems, but current noisy, intermediate-scale machines remain limited by qubit errors and available circuit sizes.
“We wanted to find a smarter approach to dealing with a major computational bottleneck in quantum modeling of fluid dynamics.”
— Chao Lu, ORNL postdoctoral researcher and study lead
Practical Speedup Remains Unreported
The report gives the reduced gate count but does not state how long the quantum processor runs took, how their outputs compared with classical results, or whether LuGo delivered a measurable speedup. It also does not specify processor-by-processor performance or error rates. Those details are needed to judge how well the method works beyond reducing circuit requirements.
It remains unclear how the algorithm would perform on larger or more realistic fluid models, or whether it can help produce more accurate predictions. The cited uses in microfluidics, groundwater and porous media are possible future applications, rather than results of the reported study.
Testing LuGo on Other Problems
Lu said the team wants to explore what acceleration LuGo might enable in other applications. The next evidence to watch for is published performance data showing how the algorithm behaves on quantum processors and whether the reduced gate count translates into useful results for fluid models. The source material does not give a timeline for those studies.
Key Questions
What is LuGo?
LuGo is an algorithm developed by ORNL researchers to streamline quantum phase estimation for a fluid-flow modeling problem by doing more calculations on classical computers before encoding data in a quantum circuit.
How much did LuGo reduce the gate requirement?
The team reports a reduction from 2 million gates to 91,000, or more than 95%, for the problem it studied.
Does this show quantum computers can model fluid flow faster?
No measured practical speedup is reported. The source describes classical circuit simulations and evaluation on quantum processors, but does not give run-time comparisons against classical fluid modeling.
What fluid-flow problem did the researchers study?
The work built on research applying quantum methods to the Hele-Shaw flow equation, which describes fluids moving between two closely spaced parallel plates.
What applications could the method have?
The researchers identify microfluidics, groundwater flow and porous media flow as possible future applications. The report does not say LuGo has yet been used to model these cases.
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